请你帮我用segnet模型实现图像语义分割的全过程代码,包含训练模型、预测新图像，用pytorch包，谢谢

好的，下面是使用SegNet模型进行图像语义分割的全过程代码，包括模型的训练和预测过程，使用PyTorch框架实现。

首先，我们需要导入必要的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, datasets
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

接下来，我们定义SegNet模型的结构：

class SegNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1):
        super(SegNet, self).__init__()
        
        self.encoder1 = nn.Sequential(
            nn.Conv2d(in_channels, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        )
        
        self.encoder2 = nn.Sequential(
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        )
        
        self.encoder3 = nn.Sequential(
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        )
        
        self.encoder4 = nn.Sequential(
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        )
        
        self.encoder5 = nn.Sequential(
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2, return_indices=True)
        )
        
        self.decoder5 = nn.Sequential(
            nn.MaxUnpool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True)
        )
        
        self.decoder4 = nn.Sequential(
            nn.MaxUnpool2d(kernel_size=2, stride=2),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True)
        )
        
        self.decoder3 = nn.Sequential(
            nn.MaxUnpool2d(kernel_size=2, stride=2),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True)
        )
        
        self.decoder2 = nn.Sequential(
            nn.MaxUnpool2d(kernel_size=2, stride=2),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )
        
        self.decoder1 = nn.Sequential(
            nn.MaxUnpool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, out_channels, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.Sigmoid()
        )
        
    def forward(self, x):
        size_1 = x.size()
        x, idx_1 = self.encoder1(x)
        size_2 = x.size()
        x, idx_2 = self.encoder2(x)
        size_3 = x.size()
        x, idx_3 = self.encoder3(x)
        size_4 = x.size()
        x, idx_4 = self.encoder4(x)
        size_5 = x.size()
        x, idx_5 = self.encoder5(x)
        
        x = self.decoder5(x, idx_5, size_5)
        x = self.decoder4(x, idx_4, size_4)
        x = self.decoder3(x, idx_3, size_3)
        x = self.decoder2(x, idx_2, size_2)
        x = self.decoder1(x, idx_1, size_1)
        
        return x

接下来，我们需要定义数据集，包括训练集和测试集：

class SegmentationDataset(Dataset):
    def __init__(self, root_dir, transform=None):
        self.images = []
        self.masks = []
        self.transform = transform
        
        for filename in os.listdir(root_dir):
            if filename.endswith(".jpg"):
                image_file = os.path.join(root_dir, filename)
                mask_file = os.path.join(root_dir, filename.split(".")[0] + "_mask.png")
                self.images.append(image_file)
                self.masks.append(mask_file)
                
    def __len__(self):
        return len(self.images)
    
    def __getitem__(self, idx):
        image = Image.open(self.images[idx])
        mask = Image.open(self.masks[idx]).convert("L")
        
        if self.transform:
            image = self.transform(image)
            mask = self.transform(mask)
            
        return image, mask

接下来，我们需要对数据进行预处理，包括数据增强、归一化等：

transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

train_dataset = SegmentationDataset("train", transform=transform)
test_dataset = SegmentationDataset("test", transform=transform)

train_dataloader = DataLoader(train_dataset, batch_size=4, shuffle=True)
test_dataloader = DataLoader(test_dataset, batch_size=4, shuffle=False)

然后，我们需要定义损失函数和优化器：

criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

接下来，我们可以开始训练模型了：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SegNet().to(device)

for epoch in range(10):
    for i, (images, masks) in enumerate(train_dataloader):
        images = images.to(device)
        masks = masks.to(device)
        
        optimizer.zero_grad()
        
        outputs = model(images)
        loss = criterion(outputs, masks)
        
        loss.backward()
        optimizer.step()
        
        if i % 10 == 0:
            print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(epoch+1, 10, i+1, len(train_dataloader), loss.item()))

训练完成后，我们可以使用训练好的模型对新图像进行预测：

def predict_image(model, image_path):
    image = Image.open(image_path)
    image_tensor = transform(image).unsqueeze(0).to(device)
    output = model(image_tensor)
    mask = output.squeeze().cpu().detach().numpy()
    mask = np.where(mask > 0.5, 255, 0).astype(np.uint8)
    mask_image = Image.fromarray(mask)
    return mask_image

image_path = "test/image.jpg"
mask_image = predict_image(model, image_path)
mask_image.save("test/mask.png")

这样，我们就完成了使用SegNet模型进行图像语义分割的全过程代码。